Learning-based demand-supply-coupled charging station location problem for electric vehicle demand management

Yang Song, Xianbiao Hu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We present a learning-based, demand-supply-coupled optimization model for the charging station location problem (CSLP), aiming to integrate the concept of electric vehicle (EV) charging demand management into the planning of charging infrastructures. In stage one, a gradient boosting-based learning model is developed to predict the charging demand of a charging station based on 15 defined features. Next, in stage two, a demand–supply-coupled CSLP model is developed to optimize the total charging usage rates of both existing and newly selected charging stations. We design a gradient-based stochastic spatial search algorithm to solve the proposed model. A case study with 6-year charging event data from Kansas City Missouri is performed. Results show that the proposed method can generate satisfactory charging demand predictions, and can increase charging usage rates by 14%, outperforming two benchmark approaches. The results of this research are poised to guide agencies in identifying optimal locations for new charging stations.

Original languageEnglish (US)
Article number103975
JournalTransportation Research Part D: Transport and Environment
Volume125
DOIs
StatePublished - Dec 2023

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Transportation
  • General Environmental Science

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